Workflow

This workflow performs differential expression analysis on single- or paired-end RNA-seq data. After adapter removal with Cutadapt, reads were mapped and gene counts were generated with STAR. Gene counts of replicates were summed up using custom script. These counts are by default used as an input for DESeq2. Alternatively, FeatureCounts output might be used as an input for further analysis. Integrated normalization and differential expression analysis was conducted with DESeq2 following standard procedure as outlined in the manual.

Click the nodes to obtain details about each step.

Other

Statistics

If the workflow has been executed in cluster/cloud, runtimes include the waiting time in the queue.

Configuration

Configuration files
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# path or URL to sample sheet (TSV format, columns: sample, condition, ...)
samples: config/samples.tsv
# path or URL to sequencing unit sheet (TSV format, columns: sample, unit, fq1, fq2)
# Units are technical replicates (e.g. lanes, or resequencing of the same biological
# sample).
units: config/units.tsv


ref:
  # Ensembl species name
  species: homo_sapiens
  # Ensembl release (make sure to take one where snpeff data is available, check 'snpEff databases' output)
  release: 100
  # Genome build
  build: GRCh38

trimming:
  activate: true

mergeReads:
  activate: false

pca:
  activate: True
  labels:
    # columns of sample sheet to use for PCA
    - condition

diffexp:
  # contrasts for the deseq2 results method
  contrasts:
    treated-vs-untreated:
      - treated
      - untreated
  model: ~condition

params:
  cutadapt-pe: 
    "--minimum-length 35 -a AGAGCACACGTCTGAACTCCAGTCAC -g AGATCGGAAGAGCACACGT -A AGAGCACACGTCTGAACTCCAGTCAC -G AGATCGGAAGAGCACACGT"
  cutadapt-se: ""
  star: ""

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